Maintaining the codebase

This repo is a copy of the original repositoy athttps://github.com/scikit-optimize/scikit-optimize/.As the original repo is now in read-only mode, i decided to continue the development on it on my own.I still have credentials for pypi, so I will publish new releases athttps://pypi.org/project/scikit-optimize/.I did my best to include all open PR since 2021 in the new release of scikit-optimize 0.10.

https://scikit-optimize.github.io/ has been moved tohttp://scikit-optimize.readthedocs.io/.

Important links

• Project websitehttps://scikit-optimize.readthedocs.io/

• Example notebooks - can be found inexamples.

• Discussion forum

• Issue tracker -https://github.com/holgern/scikit-optimize/issues

• Releases -https://pypi.python.org/pypi/scikit-optimize

• Conda feedstock -https://github.com/conda-forge/scikit-optimize-feedstock

Install

scikit-optimize requires

• Python >= 3.8

• NumPy (>= 1.20.3)

• SciPy (>= 0.19.1)

• joblib (>= 0.11)

• scikit-learn >= 1.0.0

• matplotlib >= 2.0.0

You can install the latest release with:

pip install scikit-optimize

This installs the essentials. To install plotting functionality,you can instead do:

pip install 'scikit-optimize[plots]'

This will additionally install Matplotlib.

If you’re using Anaconda platform, there is aconda-forgepackage of scikit-optimize:

conda install -c conda-forge scikit-optimize

Using conda-forge is probably the easiest way to install scikit-optimize onWindows.

Getting started

Find the minimum of the noisy functionf(x) over the range-2 < x < 2 withskopt:

importnumpyasnpfromskoptimportgp_minimizedeff(x):return(np.sin(5*x[0])*(1-np.tanh(x[0]**2))+np.random.randn()*0.1)res=gp_minimize(f,[(-2.0,2.0)])

For more control over the optimization loop you can use theskopt.Optimizerclass:

fromskoptimportOptimizeropt=Optimizer([(-2.0,2.0)])foriinrange(20):suggested=opt.ask()y=f(suggested)opt.tell(suggested,y)print('iteration:',i,suggested,y)

Read ourintroduction to bayesianoptimizationand the otherexamples.

Development

The library is still experimental and under development. Checkoutthenextmilestonefor the plans for the next release or look at someeasyissuesto get started contributing.

The development version can be installed through:

git clone https://github.com/holgern/scikit-optimize.gitcd scikit-optimizepip install -e .

Run all tests by executingpytest in the top level directory.

To only run the subset of tests with short run time, you can usepytest-m 'fast_test' (pytest-m 'slow_test' is also possible). To exclude all slow running tests trypytest-m 'not slow_test'.

This is implemented using pytestattributes. If a tests runs longer than 1 second, it is marked as slow, else as fast.

All contributors are welcome!

Pre-commit-config

pip install pre-commit

brew install pre-commitpre-commit --versionpre-commit 2.10.0

pre-commit install

pre-commit run --all-filespre-commit run --show-diff-on-failure --color=always --all-files

pre-commit autoupdatepre-commit run --show-diff-on-failure --color=always --all-files

Made possible by

The scikit-optimize project was made possible with the support of

If your employer allows you to work on scikit-optimize during the day and would likerecognition, feel free to add them to the “Made possible by” list.